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https://issues.apache.org/jira/browse/HADOOP-18707?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Nicolas PHUNG resolved HADOOP-18707.
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Fix Version/s: 3.3.4
Resolution: Workaround
> Cannot write to Azure Datalake Gen2 (abfs/abfss) after Spark 3.1.2
> ------------------------------------------------------------------
>
> Key: HADOOP-18707
> URL: https://issues.apache.org/jira/browse/HADOOP-18707
> Project: Hadoop Common
> Issue Type: Bug
> Components: fs/azure
> Affects Versions: 3.3.2, 3.3.5, 3.3.4
> Reporter: Nicolas PHUNG
> Priority: Major
> Fix For: 3.3.4
>
>
> Hello,
> I have an issue with Spark 3.3.2 & Spark 3.4.0 to write into Azure Data Lake
> Storage Gen2 (abfs/abfss scheme). I've got the following errors:
> {code:java}
> warn 13:12:47.554: StdErr from Kernel Process 23/04/19 13:12:47 ERROR
> FileFormatWriter: Aborting job
> 6a75949c-1473-4445-b8ab-d125be3f0f21.org.apache.spark.SparkException: Job
> aborted due to stage failure: Task 1 in stage 0.0 failed 1 times, most recent
> failure: Lost task 1.0 in stage 0.0 (TID 1) (myhost executor driver):
> org.apache.hadoop.util.DiskChecker$DiskErrorException: Could not find any
> valid local directory for datablock-0001- at
> org.apache.hadoop.fs.LocalDirAllocator$AllocatorPerContext.getLocalPathForWrite(LocalDirAllocator.java:462)
> at
> org.apache.hadoop.fs.LocalDirAllocator.getLocalPathForWrite(LocalDirAllocator.java:165)
> at
> org.apache.hadoop.fs.LocalDirAllocator.getLocalPathForWrite(LocalDirAllocator.java:146)
> at
> org.apache.hadoop.fs.store.DataBlocks$DiskBlockFactory.createTmpFileForWrite(DataBlocks.java:980)
> at
> org.apache.hadoop.fs.store.DataBlocks$DiskBlockFactory.create(DataBlocks.java:960)
> at
> org.apache.hadoop.fs.azurebfs.services.AbfsOutputStream.createBlockIfNeeded(AbfsOutputStream.java:262)
> at
> org.apache.hadoop.fs.azurebfs.services.AbfsOutputStream.<init>(AbfsOutputStream.java:173)
> at
> org.apache.hadoop.fs.azurebfs.AzureBlobFileSystemStore.createFile(AzureBlobFileSystemStore.java:580)
> at
> org.apache.hadoop.fs.azurebfs.AzureBlobFileSystem.create(AzureBlobFileSystem.java:301)
> at org.apache.hadoop.fs.FileSystem.create(FileSystem.java:1195) at
> org.apache.hadoop.fs.FileSystem.create(FileSystem.java:1175) at
> org.apache.parquet.hadoop.util.HadoopOutputFile.create(HadoopOutputFile.java:74)
> at
> org.apache.parquet.hadoop.ParquetFileWriter.<init>(ParquetFileWriter.java:347)
> at
> org.apache.parquet.hadoop.ParquetFileWriter.<init>(ParquetFileWriter.java:314)
> at
> org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:480)
> at
> org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:420)
> at
> org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:409)
> at
> org.apache.spark.sql.execution.datasources.parquet.ParquetOutputWriter.<init>(ParquetOutputWriter.scala:36)
> at
> org.apache.spark.sql.execution.datasources.parquet.ParquetUtils$$anon$1.newInstance(ParquetUtils.scala:490)
> at
> org.apache.spark.sql.execution.datasources.SingleDirectoryDataWriter.newOutputWriter(FileFormatDataWriter.scala:161)
> at
> org.apache.spark.sql.execution.datasources.SingleDirectoryDataWriter.<init>(FileFormatDataWriter.scala:146)
> at
> org.apache.spark.sql.execution.datasources.FileFormatWriter$.executeTask(FileFormatWriter.scala:389)
> at
> org.apache.spark.sql.execution.datasources.WriteFilesExec.$anonfun$doExecuteWrite$1(WriteFiles.scala:100)
> at
> org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:888)
> at
> org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:888)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:364) at
> org.apache.spark.rdd.RDD.iterator(RDD.scala:328) at
> org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:92) at
> org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161)
> at org.apache.spark.scheduler.Task.run(Task.scala:139) at
> org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554)
> at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1529)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
> at java.lang.Thread.run(Thread.java:748)
> Driver stacktrace: at
> org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2785)
> at
> org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2721)
> at
> org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2720)
> at
> scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
> at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49) at
> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2720)
> at
> org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1206)
> at
> org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1206)
> at scala.Option.foreach(Option.scala:407) at
> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1206)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2984)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2923)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2912)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49) at
> org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:971) at
> org.apache.spark.SparkContext.runJob(SparkContext.scala:2263) at
> org.apache.spark.sql.execution.datasources.FileFormatWriter$.$anonfun$executeWrite$4(FileFormatWriter.scala:307)
> at
> org.apache.spark.sql.execution.datasources.FileFormatWriter$.writeAndCommit(FileFormatWriter.scala:271)
> at
> org.apache.spark.sql.execution.datasources.FileFormatWriter$.executeWrite(FileFormatWriter.scala:304)
> at
> org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:190)
> at
> org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:190)
> at
> org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult$lzycompute(commands.scala:113)
> at
> org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult(commands.scala:111)
> at
> org.apache.spark.sql.execution.command.DataWritingCommandExec.executeCollect(commands.scala:125)
> at
> org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.$anonfun$applyOrElse$1(QueryExecution.scala:98)
> at
> org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$6(SQLExecution.scala:118)
> at
> org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:195)
> at
> org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:103)
> at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:827)
> at
> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:65)
> at
> org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.applyOrElse(QueryExecution.scala:98)
> at
> org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.applyOrElse(QueryExecution.scala:94)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDownWithPruning$1(TreeNode.scala:512)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:104)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDownWithPruning(TreeNode.scala:512)
> at
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.org$apache$spark$sql$catalyst$plans$logical$AnalysisHelper$$super$transformDownWithPruning(LogicalPlan.scala:31)
> at
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning(AnalysisHelper.scala:267)
> at
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning$(AnalysisHelper.scala:263)
> at
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:31)
> at
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:31)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:488)
> at
> org.apache.spark.sql.execution.QueryExecution.eagerlyExecuteCommands(QueryExecution.scala:94)
> at
> org.apache.spark.sql.execution.QueryExecution.commandExecuted$lzycompute(QueryExecution.scala:81)
> at
> org.apache.spark.sql.execution.QueryExecution.commandExecuted(QueryExecution.scala:79)
> at
> org.apache.spark.sql.execution.QueryExecution.assertCommandExecuted(QueryExecution.scala:133)
> at
> org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:856)
> at
> org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:387)
> at
> org.apache.spark.sql.DataFrameWriter.saveInternal(DataFrameWriter.scala:360)
> at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:239)
> at org.apache.spark.sql.DataFrameWriter.parquet(DataFrameWriter.scala:789)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:498) at
> py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) at
> py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:374) at
> py4j.Gateway.invoke(Gateway.java:282) at
> py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) at
> py4j.commands.CallCommand.execute(CallCommand.java:79) at
> py4j.ClientServerConnection.waitForCommands(ClientServerConnection.java:182)
> at py4j.ClientServerConnection.run(ClientServerConnection.java:106) at
> java.lang.Thread.run(Thread.java:748)Caused by:
> org.apache.hadoop.util.DiskChecker$DiskErrorException: Could not find any
> valid local directory for datablock-0001- at
> org.apache.hadoop.fs.LocalDirAllocator$AllocatorPerContext.getLocalPathForWrite(LocalDirAllocator.java:462)
> at
> org.apache.hadoop.fs.LocalDirAllocator.getLocalPathForWrite(LocalDirAllocator.java:165)
> at
> org.apache.hadoop.fs.LocalDirAllocator.getLocalPathForWrite(LocalDirAllocator.java:146)
> at
> org.apache.hadoop.fs.store.DataBlocks$DiskBlockFactory.createTmpFileForWrite(DataBlocks.java:980)
> at
> org.apache.hadoop.fs.store.DataBlocks$DiskBlockFactory.create(DataBlocks.java:960)
> at
> org.apache.hadoop.fs.azurebfs.services.AbfsOutputStream.createBlockIfNeeded(AbfsOutputStream.java:262)
> at
> org.apache.hadoop.fs.azurebfs.services.AbfsOutputStream.<init>(AbfsOutputStream.java:173)
> at
> org.apache.hadoop.fs.azurebfs.AzureBlobFileSystemStore.createFile(AzureBlobFileSystemStore.java:580)
> at
> org.apache.hadoop.fs.azurebfs.AzureBlobFileSystem.create(AzureBlobFileSystem.java:301)
> at org.apache.hadoop.fs.FileSystem.create(FileSystem.java:1195) at
> org.apache.hadoop.fs.FileSystem.create(FileSystem.java:1175) at
> org.apache.parquet.hadoop.util.HadoopOutputFile.create(HadoopOutputFile.java:74)
> at
> org.apache.parquet.hadoop.ParquetFileWriter.<init>(ParquetFileWriter.java:347)
> at
> org.apache.parquet.hadoop.ParquetFileWriter.<init>(ParquetFileWriter.java:314)
> at
> org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:480)
> at
> org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:420)
> at
> org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:409)
> at
> org.apache.spark.sql.execution.datasources.parquet.ParquetOutputWriter.<init>(ParquetOutputWriter.scala:36)
> at
> org.apache.spark.sql.execution.datasources.parquet.ParquetUtils$$anon$1.newInstance(ParquetUtils.scala:490)
> at
> org.apache.spark.sql.execution.datasources.SingleDirectoryDataWriter.newOutputWriter(FileFormatDataWriter.scala:161)
> at
> org.apache.spark.sql.execution.datasources.SingleDirectoryDataWriter.<init>(FileFormatDataWriter.scala:146)
> at
> org.apache.spark.sql.execution.datasources.FileFormatWriter$.executeTask(FileFormatWriter.scala:389)
> at
> org.apache.spark.sql.execution.datasources.WriteFilesExec.$anonfun$doExecuteWrite$1(WriteFiles.scala:100)
> at
> org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:888)
> at
> org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:888)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:364) at
> org.apache.spark.rdd.RDD.iterator(RDD.scala:328) at
> org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:92) at
> org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161)
> at org.apache.spark.scheduler.Task.run(Task.scala:139) at
> org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554)
> at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1529)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
> ... 1 more {code}
> Before that, I was able to write into Azure Data Lake Storage with Spark
> 3.1.2 with hadoop-azure 3.2.1 without encountering this error.
> Here's what I have tried but with no success:
> * Spark 3.3.2 with hadoop-azure 3.3.2
> * Spark 3.3.2 with hadoop-azure 3.3.5
> * Spark 3.4.0 with hadoop-azure 3.3.4
> * Spark 3.4.0 with hadoop-azure 3.3.5
> Regards,
> PS: I have posted the issues on Spark too
> https://issues.apache.org/jira/browse/SPARK-43188
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